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Diffstat (limited to 'build/Bonmin/include/coin/BonTMINLP.hpp')
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diff --git a/build/Bonmin/include/coin/BonTMINLP.hpp b/build/Bonmin/include/coin/BonTMINLP.hpp deleted file mode 100644 index b6d21e1..0000000 --- a/build/Bonmin/include/coin/BonTMINLP.hpp +++ /dev/null @@ -1,420 +0,0 @@ -// (C) Copyright International Business Machines Corporation and -// Carnegie Mellon University 2004, 2007 -// -// All Rights Reserved. -// This code is published under the Eclipse Public License. -// -// Authors : -// Pierre Bonami, Carnegie Mellon University, -// Carl D. Laird, Carnegie Mellon University, -// Andreas Waechter, International Business Machines Corporation -// -// Date : 12/01/2004 - -#ifndef __TMINLP_HPP__ -#define __TMINLP_HPP__ - -#include "IpUtils.hpp" -#include "IpReferenced.hpp" -#include "IpException.hpp" -#include "IpAlgTypes.hpp" -#include "CoinPackedMatrix.hpp" -#include "OsiCuts.hpp" -#include "IpTNLP.hpp" -#include "CoinError.hpp" -#include "CoinHelperFunctions.hpp" - -namespace Bonmin -{ - DECLARE_STD_EXCEPTION(TMINLP_INVALID); - DECLARE_STD_EXCEPTION(TMINLP_INVALID_VARIABLE_BOUNDS); - - /** Base class for all MINLPs that use a standard triplet matrix form - * and dense vectors. - * The class TMINLP2TNLP allows the caller to produce a viable TNLP - * from the MINLP (by relaxing binary and/or integers, or by - * fixing them), which can then be solved by Ipopt. - * - * This interface presents the problem form: - * \f[ - * \begin{array}{rl} - * &min f(x)\\ - * - * \mbox{s.t.}&\\ - * & g^L <= g(x) <= g^U\\ - * - * & x^L <= x <= x^U\\ - * \end{array} - * \f] - * Where each x_i is either a continuous, binary, or integer variable. - * If x_i is binary, the bounds [xL,xU] are assumed to be [0,1]. - * In order to specify an equality constraint, set gL_i = gU_i = - * rhs. The value that indicates "infinity" for the bounds - * (i.e. the variable or constraint has no lower bound (-infinity) - * or upper bound (+infinity)) is set through the option - * nlp_lower_bound_inf and nlp_upper_bound_inf. To indicate that a - * variable has no upper or lower bound, set the bound to - * -ipopt_inf or +ipopt_inf respectively - */ - class TMINLP : public Ipopt::ReferencedObject - { - public: - friend class TMINLP2TNLP; - /** Return statuses of algorithm.*/ - enum SolverReturn{ - SUCCESS, - INFEASIBLE, - CONTINUOUS_UNBOUNDED, - LIMIT_EXCEEDED, - USER_INTERRUPT, - MINLP_ERROR}; - /** Class to store sos constraints for model */ - struct SosInfo - { - /** Number of SOS constraints.*/ - int num; - /** Type of sos. At present Only type '1' SOS are supported by Cbc*/ - char * types; - /** priorities of sos constraints.*/ - int * priorities; - - /** \name Sparse storage of the elements of the SOS constraints.*/ - /** @{ */ - /** Total number of non zeroes in SOS constraints.*/ - int numNz; - /** For 0 <= i < nums, start[i] gives the indice of indices and weights arrays at which the description of constraints i begins..*/ - int * starts; - /** indices of elements belonging to the SOS.*/ - int * indices; - /** weights of the elements of the SOS.*/ - double * weights; - /** @} */ - /** default constructor. */ - SosInfo(); - /** Copy constructor.*/ - SosInfo(const SosInfo & source); - - - /** destructor*/ - ~SosInfo() - { - gutsOfDestructor(); - } - - - /** Reset information */ - void gutsOfDestructor(); - - }; - - /** Stores branching priorities information. */ - struct BranchingInfo - { - /**number of variables*/ - int size; - /** User set priorities on variables. */ - int * priorities; - /** User set preferered branching direction. */ - int * branchingDirections; - /** User set up pseudo costs.*/ - double * upPsCosts; - /** User set down pseudo costs.*/ - double * downPsCosts; - BranchingInfo(): - size(0), - priorities(NULL), - branchingDirections(NULL), - upPsCosts(NULL), - downPsCosts(NULL) - {} - BranchingInfo(const BranchingInfo &other) - { - gutsOfDestructor(); - size = other.size; - priorities = CoinCopyOfArray(other.priorities, size); - branchingDirections = CoinCopyOfArray(other.branchingDirections, size); - upPsCosts = CoinCopyOfArray(other.upPsCosts, size); - downPsCosts = CoinCopyOfArray(other.downPsCosts, size); - } - void gutsOfDestructor() - { - if (priorities != NULL) delete [] priorities; - priorities = NULL; - if (branchingDirections != NULL) delete [] branchingDirections; - branchingDirections = NULL; - if (upPsCosts != NULL) delete [] upPsCosts; - upPsCosts = NULL; - if (downPsCosts != NULL) delete [] downPsCosts; - downPsCosts = NULL; - } - ~BranchingInfo() - { - gutsOfDestructor(); - } - }; - - /** Class to store perturbation radii for variables in the model */ - class PerturbInfo - { - public: - /** default constructor. */ - PerturbInfo() : - perturb_radius_(NULL) - {} - - /** destructor*/ - ~PerturbInfo() - { - delete [] perturb_radius_; - } - - /** Method for setting the perturbation radii. */ - void SetPerturbationArray(Ipopt::Index numvars, const double* perturb_radius); - - /** Method for getting the array for the perturbation radii in - * order to use the values. */ - const double* GetPerturbationArray() const { - return perturb_radius_; - } - - private: - /** Copy constructor.*/ - PerturbInfo(const PerturbInfo & source); - - /** Perturbation radii for all variables. A negative value - * means that the radius has not been given. If the pointer is - * NULL, then no variables have been assigned a perturbation - * radius. */ - double* perturb_radius_; - }; - - /** Type of the variables.*/ - enum VariableType - { - CONTINUOUS, - BINARY, - INTEGER - }; - - /**@name Constructors/Destructors */ - //@{ - TMINLP(); - - /** Default destructor */ - virtual ~TMINLP(); - //@} - - /**@name methods to gather information about the MINLP */ - //@{ - /** overload this method to return the number of variables - * and constraints, and the number of non-zeros in the jacobian and - * the hessian. */ - virtual bool get_nlp_info(Ipopt::Index& n, Ipopt::Index& m, Ipopt::Index& nnz_jac_g, - Ipopt::Index& nnz_h_lag, Ipopt::TNLP::IndexStyleEnum& index_style)=0; - - /** overload this method to return scaling parameters. This is - * only called if the options are set to retrieve user scaling. - * There, use_x_scaling (or use_g_scaling) should get set to true - * only if the variables (or constraints) are to be scaled. This - * method should return true only if the scaling parameters could - * be provided. - */ - virtual bool get_scaling_parameters(Ipopt::Number& obj_scaling, - bool& use_x_scaling, Ipopt::Index n, - Ipopt::Number* x_scaling, - bool& use_g_scaling, Ipopt::Index m, - Ipopt::Number* g_scaling) - { - return false; - } - - - /** overload this method to provide the variables types. The var_types - * array will be allocated with length n. */ - virtual bool get_variables_types(Ipopt::Index n, VariableType* var_types)=0; - - /** overload this method to provide the variables linearity. - * array should be allocated with length at least n.*/ - virtual bool get_variables_linearity(Ipopt::Index n, - Ipopt::TNLP::LinearityType* var_types) = 0; - - /** overload this method to provide the constraint linearity. - * array should be allocated with length at least m.*/ - virtual bool get_constraints_linearity(Ipopt::Index m, - Ipopt::TNLP::LinearityType* const_types) = 0; - - /** overload this method to return the information about the bound - * on the variables and constraints. The value that indicates - * that a bound does not exist is specified in the parameters - * nlp_lower_bound_inf and nlp_upper_bound_inf. By default, - * nlp_lower_bound_inf is -1e19 and nlp_upper_bound_inf is - * 1e19. - * An exception will be thrown if x_l and x_u are not 0,1 for binary variables - */ - virtual bool get_bounds_info(Ipopt::Index n, Ipopt::Number* x_l, Ipopt::Number* x_u, - Ipopt::Index m, Ipopt::Number* g_l, Ipopt::Number* g_u)=0; - - /** overload this method to return the starting point. The bools - * init_x and init_lambda are both inputs and outputs. As inputs, - * they indicate whether or not the algorithm wants you to - * initialize x and lambda respectively. If, for some reason, the - * algorithm wants you to initialize these and you cannot, set - * the respective bool to false. - */ - virtual bool get_starting_point(Ipopt::Index n, bool init_x, Ipopt::Number* x, - bool init_z, Ipopt::Number* z_L, Ipopt::Number* z_U, - Ipopt::Index m, bool init_lambda, - Ipopt::Number* lambda)=0; - - /** overload this method to return the value of the objective function */ - virtual bool eval_f(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Number& obj_value)=0; - - /** overload this method to return the vector of the gradient of - * the objective w.r.t. x */ - virtual bool eval_grad_f(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Number* grad_f)=0; - - /** overload this method to return the vector of constraint values */ - virtual bool eval_g(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Index m, Ipopt::Number* g)=0; - - /** overload this method to return the jacobian of the - * constraints. The vectors iRow and jCol only need to be set - * once. The first call is used to set the structure only (iRow - * and jCol will be non-NULL, and values will be NULL) For - * subsequent calls, iRow and jCol will be NULL. */ - virtual bool eval_jac_g(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Index m, Ipopt::Index nele_jac, Ipopt::Index* iRow, - Ipopt::Index *jCol, Ipopt::Number* values)=0; - - /** overload this method to return the hessian of the - * lagrangian. The vectors iRow and jCol only need to be set once - * (during the first call). The first call is used to set the - * structure only (iRow and jCol will be non-NULL, and values - * will be NULL) For subsequent calls, iRow and jCol will be - * NULL. This matrix is symmetric - specify the lower diagonal - * only */ - virtual bool eval_h(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Number obj_factor, Ipopt::Index m, const Ipopt::Number* lambda, - bool new_lambda, Ipopt::Index nele_hess, - Ipopt::Index* iRow, Ipopt::Index* jCol, Ipopt::Number* values)=0; - /** Compute the value of a single constraint. The constraint - * number is i (starting counting from 0. */ - virtual bool eval_gi(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Index i, Ipopt::Number& gi) - { - std::cerr << "Method eval_gi not overloaded from TMINLP\n"; - throw -1; - } - /** Compute the structure or values of the gradient for one - * constraint. The constraint * number is i (starting counting - * from 0. Other things are like with eval_jac_g. */ - virtual bool eval_grad_gi(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Index i, Ipopt::Index& nele_grad_gi, Ipopt::Index* jCol, - Ipopt::Number* values) - { - std::cerr << "Method eval_grad_gi not overloaded from TMINLP\n"; - throw -1; - } - //@} - - /** @name Solution Methods */ - //@{ - /** This method is called when the algorithm is complete so the TNLP can store/write the solution */ - virtual void finalize_solution(TMINLP::SolverReturn status, - Ipopt::Index n, const Ipopt::Number* x, Ipopt::Number obj_value) =0; - //@} - - virtual const BranchingInfo * branchingInfo() const = 0; - - virtual const SosInfo * sosConstraints() const = 0; - - virtual const PerturbInfo* perturbInfo() const - { - return NULL; - } - - /** Say if has a specific function to compute upper bounds*/ - virtual bool hasUpperBoundingObjective(){ - return false;} - - /** overload this method to return the value of an alternative objective function for - upper bounding (to use it hasUpperBoundingObjective should return true).*/ - virtual bool eval_upper_bound_f(Ipopt::Index n, const Ipopt::Number* x, - Ipopt::Number& obj_value){ return false; } - - /** Used to mark constraints of the problem.*/ - enum Convexity { - Convex/** Constraint is convex.*/, - NonConvex/** Constraint is non-convex.*/, - SimpleConcave/** Constraint is concave of the simple form y >= F(x).*/}; - - /** Structure for marked non-convex constraints. With possibility of - storing index of a constraint relaxing the non-convex constraint*/ - struct MarkedNonConvex { - /** Default constructor gives "safe" values.*/ - MarkedNonConvex(): - cIdx(-1), cRelaxIdx(-1){} - /** Index of the nonconvex constraint.*/ - int cIdx; - /** Index of constraint relaxing the nonconvex constraint.*/ - int cRelaxIdx;}; - /** Structure which describes a constraints of the form - $f[ y \gt F(x) \f] - with \f$ F(x) \f$ a concave function.*/ - struct SimpleConcaveConstraint{ - /** Default constructor gives "safe" values.*/ - SimpleConcaveConstraint(): - xIdx(-1), yIdx(-1), cIdx(-1){} - /** Index of the variable x.*/ - int xIdx; - /** Index of the variable y.*/ - int yIdx; - /** Index of the constraint.*/ - int cIdx;}; - /** Get accest to constraint convexities.*/ - virtual bool get_constraint_convexities(int m, TMINLP::Convexity * constraints_convexities)const { - CoinFillN(constraints_convexities, m, TMINLP::Convex); - return true;} - /** Get dimension information on nonconvex constraints.*/ - virtual bool get_number_nonconvex(int & number_non_conv, int & number_concave) const{ - number_non_conv = 0; - number_concave = 0; - return true;} - /** Get array describing the constraints marked nonconvex in the model.*/ - virtual bool get_constraint_convexities(int number_non_conv, MarkedNonConvex * non_convs) const{ - assert(number_non_conv == 0); - return true;} - /** Fill array containing indices of simple concave constraints.*/ - virtual bool get_simple_concave_constraints(int number_concave, SimpleConcaveConstraint * simple_concave) const{ - assert(number_concave == 0); - return true;} - - /** Say if problem has a linear objective (for OA) */ - virtual bool hasLinearObjective(){return false;} - - /** Say if problem has general integer variables.*/ - bool hasGeneralInteger(); - - /** Access array describing constraint to which perspectives should be applied.*/ - virtual const int * get_const_xtra_id() const{ - return NULL; - } - protected: - /** Copy constructor */ - //@{ - /** Copy Constructor */ - TMINLP(const TMINLP&); - - /** Overloaded Equals Operator */ - void operator=(const TMINLP&); - //@} - - private: - }; - -} // namespace Ipopt - -#endif - |